Q22. Do you do any of the following when you have influenza symptoms?
# subset question data, rename columns, gather into single column
q22_df <- df %>%
select(CaseID, PPGENDER, PPAGE, ppagecat, PPETHM, PPINCIMP, PPEDUC, PPEDUCAT,
work, PPWORK, marital, PPMARIT, PPMSACAT, ppreg9, PPSTATEN, PPHOUSE, PPRENT, PPNET, Q22_1:Q22_9, weight) %>%
rename("Q22_1_Go.to.a.doctor_s.office.or.medical.clinic" = Q22_1,
"Q22_2_Decide.on.treatment.without.consulting.a.health.practitioner" = Q22_2,
"Q22_3_Search.the.internet.for.a.treatment" = Q22_3,
"Q22_4_Get.adequate.sleep" = Q22_4,
"Q22_5_Eat.nutritious.food" = Q22_5,
"Q22_6_Take-over-counter.medication.for.symptoms" = Q22_6,
"Q22_7_Take.an.antiviral.medicine" = Q22_7,
"Q22_8_Take.no.action.to.treat.the.illness" = Q22_8,
"Q22_9_Other" = Q22_9) %>%
gather(Q22_q, Q22_r, Q22_1_Go.to.a.doctor_s.office.or.medical.clinic:Q22_8_Take.no.action.to.treat.the.illness,
na.rm = T) %>%
mutate(Q22_q = as.factor(Q22_q))%>%
mutate(Q22_r = factor(Q22_r, levels = c("Always", "Sometimes", "Never")))
# survey design
options(digits = 4)
options(survey.lonely.psu = "adjust")
des22 <- svydesign(ids = ~1, weights = ~weight, data = q22_df[is.na(q22_df$weight)==F, ])
# weighted data frame
q22 <- data.frame(svytable(~Q22_q + Q22_r + PPGENDER + ppagecat + PPETHM + PPINCIMP, des22, round = T))
# plot templates
title <- ggtitle("Do you do any of the following when you have influenza symptoms?")
## main plot
p <- ggplot(q22, aes(Q22_q, weight = Freq)) + ptext
p + geom_bar(position = 'fill') + aes(fill = Q22_r) + title + coord_flip()

p2 <- ggplot(q22, aes(Q22_q, weight = Freq)) + ptext
p2 + geom_bar(position = "fill") + aes(Q22_q, fill = Q22_r) + coord_flip()

p2 + geom_bar() + aes(Q22_r, fill = Q22_r) + facet_wrap(~Q22_q) + ptext2

p2 + geom_bar() + aes(Q22_q, fill = Q22_q) + facet_wrap(~Q22_r) + ptext2

# by gender
p2 + geom_bar() + aes(PPGENDER, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By gender")

p2 + geom_bar(position = "fill") + aes(PPGENDER, fill = Q22_r) + facet_wrap(~Q22_q)

p2 + geom_bar() + aes(Q22_q, fill = PPGENDER) + facet_wrap(~Q22_r)

p2 + geom_bar(position = 'fill') + aes(Q22_q, fill = PPGENDER) + facet_wrap(~Q22_r) + ggtitle("By gender")

p2 + geom_bar() + aes(PPGENDER, fill = PPGENDER) + facet_grid(Q22_q~Q22_r) + coord_flip() + ptext2

# by age group
p2 + geom_bar() + aes(ppagecat, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By age group")

p2 + geom_bar(position = "fill") + aes(ppagecat, fill = Q22_r) + facet_wrap(~Q22_q)

p2 + geom_bar() + aes(Q22_q, fill = ppagecat) + facet_wrap(~Q22_r)

p2 + geom_bar(position = 'fill') + aes(Q22_q, fill = ppagecat) + facet_wrap(~Q22_r) + ggtitle("By age group")

p2 + geom_bar() + aes(ppagecat, fill = ppagecat) + facet_grid(Q22_q~Q22_r) + ptext2

# by ethnic group
p2 + geom_bar() + aes(PPETHM, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By ethnic group")

p2 + geom_bar(position = "fill") + aes(PPETHM, fill = Q22_r) + facet_wrap(~Q22_q)

p2 + geom_bar() + aes(Q22_q, fill = PPETHM) + facet_wrap(~Q22_r)

p2 + geom_bar(position = 'fill') + aes(Q22_q, fill = PPETHM) + facet_wrap(~Q22_r) + ggtitle("By ethnic group")

p2 + geom_bar() + aes(PPETHM, fill = PPETHM) + facet_grid(Q22_q~Q22_r) + ptext2

p2 + geom_bar() + aes(Q22_r, fill = Q22_r) + facet_grid(Q22_q~PPETHM) + ptext2

# by income
p2 + geom_bar() + aes(PPINCIMP, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By income") + ptext2

p2 + geom_bar(position = "fill") + aes(PPINCIMP, fill = Q22_r) + facet_wrap(~Q22_q) + ptext2

p2 + geom_bar() + aes(Q22_q, fill = PPINCIMP) + facet_wrap(~Q22_r)

p2 + geom_bar(position = 'fill') + aes(Q22_q, fill = PPINCIMP) + facet_wrap(~Q22_r) + ggtitle("By income group")

p2 + geom_bar() + aes(PPINCIMP, fill = PPINCIMP) + facet_grid(Q22_q~Q22_r) + ptext2

Education, work, marital status
# update weighted data frame
q22.2 <- data.frame(svytable(~Q22_q + Q22_r + PPEDUC + PPEDUCAT + work + PPWORK + marital + PPMARIT, des22, round = T))
# restate plots
p3 <- ggplot(q22.2, aes(Q22_q, weight = Freq)) + ptext
# by education
p3 + geom_bar() + aes(PPEDUCAT, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By education")

p3 + geom_bar(position = "fill") + aes(PPEDUCAT, fill = Q22_r) + facet_wrap(~Q22_q)

p3 + geom_bar() + aes(Q22_q, fill = PPEDUCAT) + facet_wrap(~Q22_r)

p3 + geom_bar(position = "fill") + aes(Q22_q, fill = PPEDUCAT) + facet_wrap(~Q22_r) + ggtitle("By education")

p3 + geom_bar() + aes(PPEDUCAT, fill = PPEDUCAT) + facet_grid(Q22_q~Q22_r) + ptext2

p3 + geom_bar() + aes(Q22_r, fill = Q22_r) + facet_grid(Q22_q~PPEDUCAT) + ptext2

# by work
p3 + geom_bar() + aes(work, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By employment status")

p3 + geom_bar(position = "fill") + aes(work, fill = Q22_r) + facet_wrap(~Q22_q)

# by marital
p3 + geom_bar() + aes(marital, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By marital status")

p3 + geom_bar(position = "fill") + aes(marital, fill = Q22_r) + facet_wrap(~Q22_q)

Metro status, region, state, house type, housing status, internet availability
# update weighted data frame
q22.3 <- data.frame(svytable(~Q22_q + Q22_r + PPMSACAT + ppreg9 + PPSTATEN + PPHOUSE + PPRENT + PPNET, des22, round = T))
# restate plots
p4 <- ggplot(q22.3, aes(Q22_q, weight = Freq)) + ptext
# by metro status
p4 + geom_bar(position = "fill") + aes(Q22_r, fill = PPMSACAT) + facet_wrap(~Q22_q) + ggtitle("By metro status")

p4 + geom_bar(position = "fill") + aes(PPMSACAT, fill = Q22_r) + facet_wrap(~Q22_q)

# by region
p4 + geom_bar(position = "fill") + aes(Q22_r, fill = ppreg9) + facet_wrap(~Q22_q) + ggtitle("By region")

p4 + geom_bar() + aes(ppreg9, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By region")

# by state
p4 + geom_bar() + aes(Q22_r, fill = PPSTATEN) + facet_wrap(~Q22_q) + ggtitle("By state")

p4 + geom_bar() + aes(PPSTATEN, fill = Q22_q) + coord_flip() + ggtitle("By state")

# by house type
p4 + geom_bar(position = "fill") + aes(Q22_r, fill = PPHOUSE) + facet_wrap(~Q22_q)

p4 + geom_bar(position = "fill") + aes(fill = PPHOUSE) + ggtitle("By house type")

# housing status
p4 + geom_bar(position = "fill") + aes(Q22_r, fill = PPHOUSE) + facet_wrap(~Q22_q)

p4 + geom_bar() + aes(PPHOUSE, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By housing")

# by internet availability
p4 + geom_bar(position = "fill") + aes(Q22_r, fill = PPNET) + facet_wrap(~Q22_q)

p4 + geom_bar(position = "fill") + aes(PPNET, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By internet availability")

Q23. Which of the following actions do you take when you have influenza symptoms to avoid someone else from getting sick?
# subset question data, rename columns, gather into single column
q23_df <- df %>%
select(CaseID, PPGENDER, PPAGE, ppagecat, PPETHM, PPINCIMP, PPEDUC, PPEDUCAT,
work, PPWORK, marital, PPMARIT, PPMSACAT, ppreg9, PPSTATEN, PPHOUSE, PPRENT, PPNET, Q23_1:Q23_11, weight) %>%
gather(Q23_q, Q23_r, Q23_1:Q23_11, na.rm = T) %>%
mutate(Q23_q = as.factor(Q23_q))
# survey design
options(digits = 4)
options(survey.lonely.psu = "adjust")
des23 <- svydesign(ids = ~1, weights = ~weight, data = q23_df[is.na(q23_df$weight)==F, ])
Gender, age, ethnicity, income
# weighted data frame
q23 <- data.frame(svytable(~Q23_q + Q23_r + PPGENDER + ppagecat + PPETHM + PPINCIMP, des23, round = T))
# plot templates
title <- ggtitle("Which of the following actions do you take when you have influenza symptoms to avoid someone else from getting sick?")
## main plot
p <- ggplot(q23, aes(Q23_q, weight = Freq)) + ptext
p + geom_bar(position = 'fill') + aes(fill = Q23_r) + title

p2 <- ggplot(q23, aes(Q23_q, weight = Freq)) + ptext
p2 + geom_bar(position = "fill") + aes(Q23_q, fill = Q23_r)

p2 + geom_bar() + aes(Q23_r, fill = Q23_r) + facet_wrap(~Q23_q) + ptext2

p2 + geom_bar() + aes(Q23_q, fill = Q23_q) + facet_wrap(~Q23_r) + ptext2

# by gender
p2 + geom_bar() + aes(PPGENDER, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By gender")

p2 + geom_bar(position = "fill") + aes(PPGENDER, fill = Q23_r) + facet_wrap(~Q23_q)

p2 + geom_bar() + aes(Q23_q, fill = PPGENDER) + facet_wrap(~Q23_r)

p2 + geom_bar(position = 'fill') + aes(Q23_q, fill = PPGENDER) + facet_wrap(~Q23_r) + ggtitle("By gender")

p2 + geom_bar() + aes(PPGENDER, fill = PPGENDER) + facet_grid(Q23_q~Q23_r) + coord_flip() + ptext2

# by age group
p2 + geom_bar() + aes(ppagecat, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By age group")

p2 + geom_bar(position = "fill") + aes(ppagecat, fill = Q23_r) + facet_wrap(~Q23_q)

p2 + geom_bar() + aes(Q23_q, fill = ppagecat) + facet_wrap(~Q23_r)

p2 + geom_bar(position = 'fill') + aes(Q23_q, fill = ppagecat) + facet_wrap(~Q23_r) + ggtitle("By age group")

p2 + geom_bar() + aes(ppagecat, fill = ppagecat) + facet_grid(Q23_q~Q23_r) + ptext2

# by ethnic group
p2 + geom_bar() + aes(PPETHM, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By ethnic group")

p2 + geom_bar(position = "fill") + aes(PPETHM, fill = Q23_r) + facet_wrap(~Q23_q)

p2 + geom_bar() + aes(Q23_q, fill = PPETHM) + facet_wrap(~Q23_r)

p2 + geom_bar(position = 'fill') + aes(Q23_q, fill = PPETHM) + facet_wrap(~Q23_r) + ggtitle("By ethnic group")

p2 + geom_bar() + aes(PPETHM, fill = PPETHM) + facet_grid(Q23_q~Q23_r) + ptext2

p2 + geom_bar() + aes(Q23_r, fill = Q23_r) + facet_grid(Q23_q~PPETHM) + ptext2

# by income
p2 + geom_bar() + aes(PPINCIMP, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By income") + ptext2

p2 + geom_bar(position = "fill") + aes(PPINCIMP, fill = Q23_r) + facet_wrap(~Q23_q) + ptext2

p2 + geom_bar() + aes(Q23_q, fill = PPINCIMP) + facet_wrap(~Q23_r)

p2 + geom_bar(position = 'fill') + aes(Q23_q, fill = PPINCIMP) + facet_wrap(~Q23_r) + ggtitle("By income group")

p2 + geom_bar() + aes(PPINCIMP, fill = PPINCIMP) + facet_grid(Q23_q~Q23_r) + ptext2

Education, work, marital status
# update weighted data frame
q23.2 <- data.frame(svytable(~Q23_q + Q23_r + PPEDUC + PPEDUCAT + work + PPWORK + marital + PPMARIT, des23, round = T))
# restate plots
p3 <- ggplot(q23.2, aes(Q23_q, weight = Freq)) + ptext
# by education
p3 + geom_bar() + aes(PPEDUCAT, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By education")

p3 + geom_bar(position = "fill") + aes(PPEDUCAT, fill = Q23_r) + facet_wrap(~Q23_q)

p3 + geom_bar() + aes(Q23_q, fill = PPEDUCAT) + facet_wrap(~Q23_r)

p3 + geom_bar(position = "fill") + aes(Q23_q, fill = PPEDUCAT) + facet_wrap(~Q23_r) + ggtitle("By education")

p3 + geom_bar() + aes(PPEDUCAT, fill = PPEDUCAT) + facet_grid(Q23_q~Q23_r) + ptext2

p3 + geom_bar() + aes(Q23_r, fill = Q23_r) + facet_grid(Q23_q~PPEDUCAT) + ptext2

# by work
p3 + geom_bar() + aes(work, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By employment status")

p3 + geom_bar(position = "fill") + aes(work, fill = Q23_r) + facet_wrap(~Q23_q)

# by marital
p3 + geom_bar() + aes(marital, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By marital status")

p3 + geom_bar(position = "fill") + aes(marital, fill = Q23_r) + facet_wrap(~Q23_q)

Metro status, region, state, house type, housing status, internet availability
# update weighted data frame
q23.3 <- data.frame(svytable(~Q23_q + Q23_r + PPMSACAT + ppreg9 + PPSTATEN + PPHOUSE + PPRENT + PPNET, des23, round = T))
# restate plots
p4 <- ggplot(q23.3, aes(Q23_q, weight = Freq)) + ptext
# by metro status
p4 + geom_bar(position = "fill") + aes(Q23_r, fill = PPMSACAT) + facet_wrap(~Q23_q) + ggtitle("By metro status")

p4 + geom_bar(position = "fill") + aes(PPMSACAT, fill = Q23_r) + facet_wrap(~Q23_q)

# by region
p4 + geom_bar(position = "fill") + aes(Q23_r, fill = ppreg9) + facet_wrap(~Q23_q) + ggtitle("By region")

p4 + geom_bar() + aes(ppreg9, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By region")

# by state
p4 + geom_bar() + aes(Q23_r, fill = PPSTATEN) + facet_wrap(~Q23_q) + ggtitle("By state")

p4 + geom_bar() + aes(PPSTATEN, fill = Q23_q) + coord_flip() + ggtitle("By state")

# by house type
p4 + geom_bar(position = "fill") + aes(Q23_r, fill = PPHOUSE) + facet_wrap(~Q23_q)

p4 + geom_bar(position = "fill") + aes(fill = PPHOUSE) + ggtitle("By house type")

# housing status
p4 + geom_bar(position = "fill") + aes(Q23_r, fill = PPHOUSE) + facet_wrap(~Q23_q)

p4 + geom_bar() + aes(PPHOUSE, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By housing")

# by internet availability
p4 + geom_bar(position = "fill") + aes(Q23_r, fill = PPNET) + facet_wrap(~Q23_q)

p4 + geom_bar(position = "fill") + aes(PPNET, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By internet availability")

---
title: 'Q22-23'
output:
  html_notebook: 
    theme: paper
    toc: yes
    toc_depth: 2
  html_document:
    fig_height: 5
    fig_width: 7
    theme: paper
    keep_md: yes
    toc: yes
    toc_depth: 2
---

Questions 22, 23, 25

```{r setup, include=F}
## Setup.
knitr::opts_chunk$set(echo = T, cache = T, cache.comments = F, warning = F, message = F, size = "small")
rm(list = ls(all.names = T))
library(rmarkdown); library(knitr); library(gridExtra)
library(tidyr); library(dplyr); library(ggplot2); library(survey)
```

```{r load-data, include=F}
## Load data.
load("~/git/flu-survey/data/cleaning2.RData")
load("~/git/flu-survey/data/recoding.RData")  # load "datar"
df <- datar  # recoded variables
```

```{r group-data, include=F}
## Regroup variables.
# income
income.map <- c(rep("under $10k", 3), rep("$10k to $25k", 4),
                rep("$25k to $50k", 4), rep("$50k to $75k", 2),
                rep("$75k to $100k", 2), rep("$100k to $150k", 2),
                rep("over $150k", 2))
df$income <- code(datar$PPINCIMP, income.map, "under $10k")
income.lab <- c("under $10k", "$10k to $25k", "$25k to $50k",
                "$50k to $75k", "$75k to $100k", "$100k to $150k",
                "over $150k")
df$income <- factor(df$income, levels = income.lab)

# marital staus
marital.map <- c("single", "partnered", "partnered", "single", "single", "single")
df$marital <- code(dataf$PPMARIT, marital.map, "single")

# work status
work.map <- c(rep("unemployed", 5),
              rep("employed", 2))
df$work <- code(dataf$PPWORK, work.map, "unemployed")
```

```{r des-survey}
## Create survey object.
options(digits = 4)
options(survey.lonely.psu = "adjust")

des <- svydesign(ids = ~1, weights = ~weight, data = df[is.na(df$weight)==F, ])
```

```{r plot-temp, include=F}
## Create ggplot templates.
ptext <- theme(axis.text = element_text(size = rel(0.9)),
               axis.text.x = element_text(angle = 45, hjust = 1))
ptext2 <- ptext + theme(axis.text.x = element_blank())
```


## Q22. Do you do any of the following when you have influenza symptoms?


```{r q22-data}
# subset question data, rename columns, gather into single column
q22_df <- df %>%
  select(CaseID, PPGENDER, PPAGE, ppagecat, PPETHM, PPINCIMP, PPEDUC, PPEDUCAT,
         work, PPWORK, marital, PPMARIT, PPMSACAT, ppreg9, PPSTATEN, PPHOUSE, PPRENT, PPNET, Q22_1:Q22_9, weight) %>%
  rename("Q22_1_Go.to.a.doctor_s.office.or.medical.clinic" = Q22_1,
         "Q22_2_Decide.on.treatment.without.consulting.a.health.practitioner" = Q22_2,
         "Q22_3_Search.the.internet.for.a.treatment" = Q22_3,
         "Q22_4_Get.adequate.sleep" = Q22_4,
         "Q22_5_Eat.nutritious.food" = Q22_5,
         "Q22_6_Take-over-counter.medication.for.symptoms" = Q22_6,
         "Q22_7_Take.an.antiviral.medicine" = Q22_7,
         "Q22_8_Take.no.action.to.treat.the.illness" = Q22_8,
         "Q22_9_Other" = Q22_9) %>%
  gather(Q22_q, Q22_r, Q22_1_Go.to.a.doctor_s.office.or.medical.clinic:Q22_8_Take.no.action.to.treat.the.illness,
         na.rm = T) %>%
  mutate(Q22_q = as.factor(Q22_q))%>%
  mutate(Q22_r = factor(Q22_r, levels = c("Always", "Sometimes", "Never")))


# survey design
options(digits = 4)
options(survey.lonely.psu = "adjust")
des22 <- svydesign(ids = ~1, weights = ~weight, data = q22_df[is.na(q22_df$weight)==F, ])
```

```{r q22-plot-1, fig.height=3, fig.width=8}
# weighted data frame
q22 <- data.frame(svytable(~Q22_q + Q22_r + PPGENDER + ppagecat + PPETHM + PPINCIMP, des22, round = T))

# plot templates
title <- ggtitle("Do you do any of the following when you have influenza symptoms?")

## main plot
p <- ggplot(q22, aes(Q22_q, weight = Freq)) + ptext
p + geom_bar(position = 'fill') + aes(fill = Q22_r) + title + coord_flip()

```


```{r q22-plot-1b, fig.height=4, fig.width=8}
p2 <- ggplot(q22, aes(Q22_q, weight = Freq)) + ptext
p2 + geom_bar(position = "fill") + aes(Q22_q, fill = Q22_r) + coord_flip()
p2 + geom_bar() + aes(Q22_r, fill = Q22_r) + facet_wrap(~Q22_q) + ptext2
p2 + geom_bar() + aes(Q22_q, fill = Q22_q) + facet_wrap(~Q22_r) + ptext2

```


```{r}
# by gender
p2 + geom_bar() + aes(PPGENDER, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By gender")
p2 + geom_bar(position = "fill") + aes(PPGENDER, fill = Q22_r) + facet_wrap(~Q22_q)
p2 + geom_bar() + aes(Q22_q, fill = PPGENDER) + facet_wrap(~Q22_r)
p2 + geom_bar(position = 'fill') + aes(Q22_q, fill = PPGENDER) + facet_wrap(~Q22_r) + ggtitle("By gender")
p2 + geom_bar() + aes(PPGENDER, fill = PPGENDER) + facet_grid(Q22_q~Q22_r) + coord_flip() + ptext2
```


```{r}
# by age group
p2 + geom_bar() + aes(ppagecat, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By age group")
p2 + geom_bar(position = "fill") + aes(ppagecat, fill = Q22_r) + facet_wrap(~Q22_q)
p2 + geom_bar() + aes(Q22_q, fill = ppagecat) + facet_wrap(~Q22_r)
p2 + geom_bar(position = 'fill') + aes(Q22_q, fill = ppagecat) + facet_wrap(~Q22_r) + ggtitle("By age group")
p2 + geom_bar() + aes(ppagecat, fill = ppagecat) + facet_grid(Q22_q~Q22_r) + ptext2
```


```{r}
# by ethnic group
p2 + geom_bar() + aes(PPETHM, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By ethnic group")
p2 + geom_bar(position = "fill") + aes(PPETHM, fill = Q22_r) + facet_wrap(~Q22_q)
p2 + geom_bar() + aes(Q22_q, fill = PPETHM) + facet_wrap(~Q22_r)
p2 + geom_bar(position = 'fill') + aes(Q22_q, fill = PPETHM) + facet_wrap(~Q22_r) + ggtitle("By ethnic group")
p2 + geom_bar() + aes(PPETHM, fill = PPETHM) + facet_grid(Q22_q~Q22_r) + ptext2
p2 + geom_bar() + aes(Q22_r, fill = Q22_r) + facet_grid(Q22_q~PPETHM) + ptext2
```


```{r}
# by income
p2 + geom_bar() + aes(PPINCIMP, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By income") + ptext2
p2 + geom_bar(position = "fill") + aes(PPINCIMP, fill = Q22_r) + facet_wrap(~Q22_q) + ptext2
p2 + geom_bar() + aes(Q22_q, fill = PPINCIMP) + facet_wrap(~Q22_r)
p2 + geom_bar(position = 'fill') + aes(Q22_q, fill = PPINCIMP) + facet_wrap(~Q22_r) + ggtitle("By income group")
p2 + geom_bar() + aes(PPINCIMP, fill = PPINCIMP) + facet_grid(Q22_q~Q22_r) + ptext2

```

### Education, work, marital status

```{r q22-plot-2}
# update weighted data frame
q22.2 <- data.frame(svytable(~Q22_q + Q22_r + PPEDUC + PPEDUCAT + work + PPWORK + marital + PPMARIT, des22, round = T))

# restate plots
p3 <- ggplot(q22.2, aes(Q22_q, weight = Freq)) + ptext
```

```{r q22-plot-2b}
# by education
p3 + geom_bar() + aes(PPEDUCAT, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By education")
p3 + geom_bar(position = "fill") + aes(PPEDUCAT, fill = Q22_r) + facet_wrap(~Q22_q)
p3 + geom_bar() + aes(Q22_q, fill = PPEDUCAT) + facet_wrap(~Q22_r)
p3 + geom_bar(position = "fill") + aes(Q22_q, fill = PPEDUCAT) + facet_wrap(~Q22_r) + ggtitle("By education")
p3 + geom_bar() + aes(PPEDUCAT, fill = PPEDUCAT) + facet_grid(Q22_q~Q22_r) + ptext2
p3 + geom_bar() + aes(Q22_r, fill = Q22_r) + facet_grid(Q22_q~PPEDUCAT) + ptext2
```


```{r}
# by work
p3 + geom_bar() + aes(work, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By employment status")
p3 + geom_bar(position = "fill") + aes(work, fill = Q22_r) + facet_wrap(~Q22_q)
```


```{r}
# by marital
p3 + geom_bar() + aes(marital, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By marital status")
p3 + geom_bar(position = "fill") + aes(marital, fill = Q22_r) + facet_wrap(~Q22_q)

```

### Metro status, region, state, house type, housing status, internet availability

```{r q22-plot-3}
# update weighted data frame
q22.3 <- data.frame(svytable(~Q22_q + Q22_r + PPMSACAT + ppreg9 + PPSTATEN + PPHOUSE + PPRENT + PPNET, des22, round = T))

# restate plots
p4 <- ggplot(q22.3, aes(Q22_q, weight = Freq)) + ptext
```

```{r q22-plot-3b}
# by metro status
p4 + geom_bar(position = "fill") + aes(Q22_r, fill = PPMSACAT) + facet_wrap(~Q22_q) + ggtitle("By metro status")
p4 + geom_bar(position = "fill") + aes(PPMSACAT, fill = Q22_r) + facet_wrap(~Q22_q)

# by region
p4 + geom_bar(position = "fill") + aes(Q22_r, fill = ppreg9) + facet_wrap(~Q22_q) + ggtitle("By region")
p4 + geom_bar() + aes(ppreg9, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By region")

# by state
p4 + geom_bar() + aes(Q22_r, fill = PPSTATEN) + facet_wrap(~Q22_q) + ggtitle("By state")
p4 + geom_bar() + aes(PPSTATEN, fill = Q22_q) + coord_flip() + ggtitle("By state")
```


```{r}
# by house type
p4 + geom_bar(position = "fill") + aes(Q22_r, fill = PPHOUSE) + facet_wrap(~Q22_q)
p4 + geom_bar(position = "fill") + aes(fill = PPHOUSE) + ggtitle("By house type")

# housing status
p4 + geom_bar(position = "fill") + aes(Q22_r, fill = PPHOUSE) + facet_wrap(~Q22_q)
p4 + geom_bar() + aes(PPHOUSE, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By housing")

# by internet availability
p4 + geom_bar(position = "fill") + aes(Q22_r, fill = PPNET) + facet_wrap(~Q22_q)
p4 + geom_bar(position = "fill") + aes(PPNET, fill = Q22_r) + facet_wrap(~Q22_q) + ggtitle("By internet availability")

```





## Q23. Which of the following actions do you take when you have influenza symptoms to avoid someone else from getting sick?

```{r q23-data}
# subset question data, rename columns, gather into single column
q23_df <- df %>%
  select(CaseID, PPGENDER, PPAGE, ppagecat, PPETHM, PPINCIMP, PPEDUC, PPEDUCAT,
         work, PPWORK, marital, PPMARIT, PPMSACAT, ppreg9, PPSTATEN, PPHOUSE, PPRENT, PPNET, Q23_1:Q23_11, weight) %>%
  gather(Q23_q, Q23_r, Q23_1:Q23_11, na.rm = T) %>%
  mutate(Q23_q = as.factor(Q23_q))

# survey design
options(digits = 4)
options(survey.lonely.psu = "adjust")
des23 <- svydesign(ids = ~1, weights = ~weight, data = q23_df[is.na(q23_df$weight)==F, ])
```

### Gender, age, ethnicity, income

```{r q23-plot-1}
# weighted data frame
q23 <- data.frame(svytable(~Q23_q + Q23_r + PPGENDER + ppagecat + PPETHM + PPINCIMP, des23, round = T))

# plot templates
title <- ggtitle("Which of the following actions do you take when you have influenza symptoms to avoid someone else from getting sick?")

## main plot
p <- ggplot(q23, aes(Q23_q, weight = Freq)) + ptext
p + geom_bar(position = 'fill') + aes(fill = Q23_r) + title
```

```{r q23-plot-1b}
p2 <- ggplot(q23, aes(Q23_q, weight = Freq)) + ptext
p2 + geom_bar(position = "fill") + aes(Q23_q, fill = Q23_r)
p2 + geom_bar() + aes(Q23_r, fill = Q23_r) + facet_wrap(~Q23_q) + ptext2
p2 + geom_bar() + aes(Q23_q, fill = Q23_q) + facet_wrap(~Q23_r) + ptext2

# by gender
p2 + geom_bar() + aes(PPGENDER, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By gender")
p2 + geom_bar(position = "fill") + aes(PPGENDER, fill = Q23_r) + facet_wrap(~Q23_q)
p2 + geom_bar() + aes(Q23_q, fill = PPGENDER) + facet_wrap(~Q23_r)
p2 + geom_bar(position = 'fill') + aes(Q23_q, fill = PPGENDER) + facet_wrap(~Q23_r) + ggtitle("By gender")
p2 + geom_bar() + aes(PPGENDER, fill = PPGENDER) + facet_grid(Q23_q~Q23_r) + coord_flip() + ptext2

# by age group
p2 + geom_bar() + aes(ppagecat, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By age group")
p2 + geom_bar(position = "fill") + aes(ppagecat, fill = Q23_r) + facet_wrap(~Q23_q)
p2 + geom_bar() + aes(Q23_q, fill = ppagecat) + facet_wrap(~Q23_r)
p2 + geom_bar(position = 'fill') + aes(Q23_q, fill = ppagecat) + facet_wrap(~Q23_r) + ggtitle("By age group")
p2 + geom_bar() + aes(ppagecat, fill = ppagecat) + facet_grid(Q23_q~Q23_r) + ptext2

# by ethnic group
p2 + geom_bar() + aes(PPETHM, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By ethnic group")
p2 + geom_bar(position = "fill") + aes(PPETHM, fill = Q23_r) + facet_wrap(~Q23_q)
p2 + geom_bar() + aes(Q23_q, fill = PPETHM) + facet_wrap(~Q23_r)
p2 + geom_bar(position = 'fill') + aes(Q23_q, fill = PPETHM) + facet_wrap(~Q23_r) + ggtitle("By ethnic group")
p2 + geom_bar() + aes(PPETHM, fill = PPETHM) + facet_grid(Q23_q~Q23_r) + ptext2
p2 + geom_bar() + aes(Q23_r, fill = Q23_r) + facet_grid(Q23_q~PPETHM) + ptext2
```


```{r}
# by income
p2 + geom_bar() + aes(PPINCIMP, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By income") + ptext2
p2 + geom_bar(position = "fill") + aes(PPINCIMP, fill = Q23_r) + facet_wrap(~Q23_q) + ptext2
p2 + geom_bar() + aes(Q23_q, fill = PPINCIMP) + facet_wrap(~Q23_r)
p2 + geom_bar(position = 'fill') + aes(Q23_q, fill = PPINCIMP) + facet_wrap(~Q23_r) + ggtitle("By income group")
p2 + geom_bar() + aes(PPINCIMP, fill = PPINCIMP) + facet_grid(Q23_q~Q23_r) + ptext2

```

### Education, work, marital status

```{r q23-plot-2}
# update weighted data frame
q23.2 <- data.frame(svytable(~Q23_q + Q23_r + PPEDUC + PPEDUCAT + work + PPWORK + marital + PPMARIT, des23, round = T))

# restate plots
p3 <- ggplot(q23.2, aes(Q23_q, weight = Freq)) + ptext
```

```{r q23-plot-2b}
# by education
p3 + geom_bar() + aes(PPEDUCAT, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By education")
p3 + geom_bar(position = "fill") + aes(PPEDUCAT, fill = Q23_r) + facet_wrap(~Q23_q)
p3 + geom_bar() + aes(Q23_q, fill = PPEDUCAT) + facet_wrap(~Q23_r)
p3 + geom_bar(position = "fill") + aes(Q23_q, fill = PPEDUCAT) + facet_wrap(~Q23_r) + ggtitle("By education")
p3 + geom_bar() + aes(PPEDUCAT, fill = PPEDUCAT) + facet_grid(Q23_q~Q23_r) + ptext2
p3 + geom_bar() + aes(Q23_r, fill = Q23_r) + facet_grid(Q23_q~PPEDUCAT) + ptext2
```

```{r}
# by work
p3 + geom_bar() + aes(work, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By employment status")
p3 + geom_bar(position = "fill") + aes(work, fill = Q23_r) + facet_wrap(~Q23_q)
```

```{r}
# by marital
p3 + geom_bar() + aes(marital, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By marital status")
p3 + geom_bar(position = "fill") + aes(marital, fill = Q23_r) + facet_wrap(~Q23_q)

```

### Metro status, region, state, house type, housing status, internet availability

```{r q23-plot-3}
# update weighted data frame
q23.3 <- data.frame(svytable(~Q23_q + Q23_r + PPMSACAT + ppreg9 + PPSTATEN + PPHOUSE + PPRENT + PPNET, des23, round = T))

# restate plots
p4 <- ggplot(q23.3, aes(Q23_q, weight = Freq)) + ptext
```

```{r q23-plot-3b}
# by metro status
p4 + geom_bar(position = "fill") + aes(Q23_r, fill = PPMSACAT) + facet_wrap(~Q23_q) + ggtitle("By metro status")
p4 + geom_bar(position = "fill") + aes(PPMSACAT, fill = Q23_r) + facet_wrap(~Q23_q)
```

```{r}
# by region
p4 + geom_bar(position = "fill") + aes(Q23_r, fill = ppreg9) + facet_wrap(~Q23_q) + ggtitle("By region")
p4 + geom_bar() + aes(ppreg9, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By region")

# by state
p4 + geom_bar() + aes(Q23_r, fill = PPSTATEN) + facet_wrap(~Q23_q) + ggtitle("By state")
p4 + geom_bar() + aes(PPSTATEN, fill = Q23_q) + coord_flip() + ggtitle("By state")
```

```{r}
# by house type
p4 + geom_bar(position = "fill") + aes(Q23_r, fill = PPHOUSE) + facet_wrap(~Q23_q)
p4 + geom_bar(position = "fill") + aes(fill = PPHOUSE) + ggtitle("By house type")

# housing status
p4 + geom_bar(position = "fill") + aes(Q23_r, fill = PPHOUSE) + facet_wrap(~Q23_q)
p4 + geom_bar() + aes(PPHOUSE, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By housing")
```

```{r}
# by internet availability
p4 + geom_bar(position = "fill") + aes(Q23_r, fill = PPNET) + facet_wrap(~Q23_q)
p4 + geom_bar(position = "fill") + aes(PPNET, fill = Q23_r) + facet_wrap(~Q23_q) + ggtitle("By internet availability")

```




